Systematically screening large amounts of textual data is time-consuming and often tiresome. The rapidly evolving field of Artificial Intelligence (AI) has allowed the development of AI-aided pipelines that assist in finding relevant texts for search tasks. A well-established approach to increasing efficiency is screening prioritization via Active Learning.
The Active learning for Systematic Reviews (ASReview) project, published in Nature Machine Intelligence implements different machine learning algorithms that interactively query the researcher. ASReview LAB is designed to accelerate the step of screening textual data with a minimum of records to be read by a human with no or very few false negatives. ASReview LAB will save time, increase the quality of output and strengthen the transparency of work when screening large amounts of textual data to retrieve relevant information. Active Learning will support decision-making in any discipline or industry.
ASReview software implements three different modes:
- Oracle Screen textual data in interaction with the active learning model. The reviewer is the 'oracle', making the labeling decisions.
- Exploration Explore or demonstrate ASReview LAB with a completely labeled dataset. This mode is suitable for teaching purposes.
- Simulation Evaluate the performance of active learning models on fully labeled data. Simulations can be run in ASReview LAB or via the command line interface with more advanced options.